Using .NET Hardware Intrinsics API to accelerate machine learning scenarios

This week's blog post is by Brian Lui, one of our summer interns on the .NET team, who's been hard at work. Over to Brian: Hello everyone! This summer I interned in the .NET team, working on ML.NET, an open-source machine learning platform which enables .NET developers to build and use machine learning models in their .NET applications. The ML.NET 0.6 release just shipped and you can try it out today. At the start of my internship, ML.NET code was already relying on vectorization for performance, using a native code library. This was an opportunity to reimplement an existing codebase in managed code, using .NET Hardware Intrinsics for vectorization, and compare results. What is vectorization, and what are SIMD, SSE, and AVX? Vectorization is a name used for applying the same operation to multiple elements of an array simultaneously. On the x86/x64 platform, vectorization can be achieved by using Single Instruction Multiple Data (SIMD) CPU instructions to operate on array-like objects. SSE (Streaming SIMD Extensions) and AVX (Advanced Vector Extensions) are the names for SIMD instruction set extensions to the x86 architecture.

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